Reconstruct and Visualise Hierarchical Relationships in Whole Slide Images

Extracting hierarchical properties from Whole Slide Images automatically and expanding the possibilities of visualising data from digital pathology will not only drastically improve speed and accuracy of the pathologists' work but also simplify the necessary pre-processing steps for machine learning tool-chains. The introduced pipeline identifies and converts areas of interest into binary masks and finds groups of areas that share similar locations using k-Means. This grouping is evaluated by the Silhouette Score which serves as a measure of confidence for the separability of clusters. Found objects are compared using structural similarities and HU-Moments. These results are then stored as measures of similarities creating virtual groups of the most similar objects. Finally, the information on similarities combined with further structural parameters are visualised.